CN116579902A - Digital park electric carbon data mapping method, system, equipment and storage medium - Google Patents
Digital park electric carbon data mapping method, system, equipment and storage medium Download PDFInfo
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Abstract
The application provides a digital park electric carbon data mapping method, a system, equipment and a storage medium, which realize mapping of a real physical park in a digital space by carrying out three steps of data cleaning, multi-time scale data fusion and electric carbon coupling conversion on park data. The park data comprise electric quantity information such as distributed new energy, building energy, energy storage, charging piles and the like, park basic information, economic information, carbon sink and other information. And finally, by using the electric carbon mapping method of the digital park, accurate park carbon emission and carbon emission intensity calculation results can be obtained, and effective support is provided for constructing electric carbon analysis and carbon peak carbon neutralization prediction of the digital park.
Description
Technical Field
The application belongs to the technical field of low carbon, and particularly relates to a method, a system, equipment and a storage medium for mapping electric carbon data of a digital park.
Background
The garden has various carriers with different properties, the carbon emission types and the current situation are complex and various, different carbon emission sources and corresponding carbon emission indexes need to be combed, and then an individual and global digital twin electric carbon monitoring system of the garden is established, so that electric power companies, governments and users are scientifically guided to take reasonable carbon reduction measures. The garden is also an important gripper for realizing double carbon, but at present, more electric carbon monitoring of the garden is embodied in informationized intelligent management and energy monitoring, an electric carbon data mapping system is urgently needed to be established, digital accurate mapping is carried out on carbon emission and carbon emission intensity, full-value chain carbon emission data is opened, a carbon market is connected, and reliable data platform support is provided for realizing carbon neutralization targets of the garden. Therefore, how to map the electric carbon data of the digital park to obtain accurate carbon emission and carbon emission intensity data is a technical problem to be solved by the application.
Disclosure of Invention
In order to solve the problems, the application provides a digital garden electric carbon data mapping method, a system, equipment and a storage medium, which can obtain accurate calculation results of the carbon emission amount and the carbon emission intensity of a garden.
In order to achieve the above object, the present application is realized by the following technical scheme:
the application relates to a digital park electric carbon data mapping method, which comprises the following steps:
step 1, acquiring park data;
step 2, cleaning the data of the park;
step 3, carrying out multi-time scale data fusion processing on the electric quantity information of the park data;
and 4, performing electric carbon coupling conversion operation, and calculating to obtain the carbon emission and the carbon emission intensity of the park.
The application further improves that: the park data in the step 1 are electric quantity information and other information, wherein the electric quantity information comprises distributed new energy information, building energy information, energy storage information and charging pile information; other information includes campus information, equipment information, economic information, and carbon sequestration information.
The application further improves that: the specific operation steps in the step 2 are as follows:
step 2.1, filling missing values of the electrical quantity information in the park data;
filling missing data by means of the average value for the uniformly distributed electrical quantity information data;
for the electrical quantity information data which are distributed randomly, filling the data by a Lagrange interpolation method, wherein the Lagrange interpolation method comprises the following expression:
wherein ,pn (x) Is a fitting polynomial, n is the number of sampling points of a certain electric quantity data, (x) i ,y i ) Data for a single point, where x i Representing time information represented by the i-th point of the electrical quantity, y i A value indicating the i-th point of the electric quantity, L n (x) The method comprises the steps of interpolating a polynomial for Lagrange, wherein j is a time sequence number corresponding to a missing value to be filled;
step 2.2, when the equipment information in the park data has the data missing condition, cleaning is performed in a continuous value judging mode, wherein the expression is as follows:
wherein ,Rt For the equipment information at time t, R t-1 and Rt+1 Device state information, R, at time t-1 and time t+1, respectively N Rated information for the device.
The application further improves that: the step 3 specifically comprises the following steps: selecting fixed data step length as lambda, when the time period lambda of the data to be fused Y When the time step is larger than the fixed calculation step length lambda, filling the data by adopting a Lagrange interpolation method, so that the time step length of the data is equal to lambda; when the time period lambda of the data to be fused Y When the calculated step length lambda is smaller than the fixed calculation step length lambda, the data are fused through the following algorithm:
wherein ,t+lambda for data fusion Y A data value of the time of day.
The application further improves that: the step 4 is specifically as follows:
step 4.1, importing an electric carbon factor library comprising power grid average electric carbon factor data kappa 1 And carbon sink electrical carbon factor data κ 2 ；
And 4.2, calculating the carbon emission and the carbon emission intensity of the park scene, wherein the expression is as follows:
C m (Γ)＝κ 1 [I p (Γ)-I r (Γ)]-κ 2 C e (Γ)
I p (Γ)＝I b (Γ)+I v (Γ)+I o (Γ)-I e (Γ)
wherein ,Cm (Γ) is the carbon emission of the park in period Γ, I p (Γ) is the total power consumption of the park in period Γ, I r (Γ) is the energy generation amount of the distributed new energy of the campuses in the period Γ, C e (Γ) is the total amount of carbon sequestration in the park during period Γ, I b (Γ) is the total electricity consumption of the building in the campus in period Γ, I v (Γ) is the total power consumption of the electric automobile in the campuses of the period Γ, I o (Γ) is the other electricity consumption of the park in period Γ, I e (Γ) is the energy storage and discharge capacity of the building in the park in period Γ, C o (Γ) is the carbon emission intensity of the park in period Γ, J c And (Γ) is the total yield value of the campus during period Γ.
The application discloses a digital park electric carbon data mapping system, which comprises a data acquisition module, a data cleaning module, a multi-time-scale data fusion module, an electric carbon factor storage module and a scene carbon row calculation module;
the data acquisition module is used for acquiring park data comprising electric quantity information and other information, wherein the electric quantity information comprises distributed new energy information, building energy information, energy storage information and charging pile information; other information includes campus information, equipment information, economic information, and carbon sequestration information;
the data cleaning module is used for cleaning missing data of the electrical quantity information and the equipment information;
the multi-time scale data fusion module fuses the electrical quantity information of the park data;
the electric carbon factor library storage module is used for storing electric carbon factor data, including power grid average electric carbon factor data kappa 1 And carbon sink electrical carbon factor data κ 2 ；
The scene carbon emission calculation module is used for calculating the carbon emission and the carbon emission intensity of the park scene, and the calculation is as follows:
C m (Γ)＝κ 1 [I p (Γ)-I r (Γ)]-κ 2 C e (Γ)
I p (Γ)＝I b (Γ)+I v (Γ)+I o (Γ)-I e (Γ)
wherein ,Cm (Γ) is the carbon emission of the park in period Γ, I p (Γ) is the total power consumption of the park in period Γ, I r (Γ) is the energy generation amount of the distributed new energy of the campuses in the period Γ, C e (Γ) is the total amount of carbon sequestration in the park during period Γ, I b (Γ) is the total electricity consumption of the building in the campus in period Γ, I v (Γ) is the total power consumption of the electric automobile in the campuses of the period Γ, I o (Γ) is the other electricity consumption of the park in period Γ, I e (Γ) is the energy storage and discharge capacity of the building in the park in period Γ, C o (Γ) is the carbon emission intensity of the park in period Γ, J c And (Γ) is the total yield value of the campus during period Γ.
The application further improves that: the data cleaning module specifically performs the following operations:
filling missing data by means of the average value for the uniformly distributed electrical quantity information data;
for the electrical quantity information data which are distributed randomly, filling the data by a Lagrange interpolation method, wherein the Lagrange interpolation method comprises the following expression:
wherein ,pn (x) Is a fitting polynomial, n is the number of sampling points of a certain electric quantity data, (x) i ,y i ) Data for a single point, where x i Representing time information represented by the i-th point of the electrical quantity, y i Indicating the i-th point of the electrical quantityThe value of L n (x) The method comprises the steps of interpolating a polynomial for Lagrange, wherein j is a time sequence number corresponding to a missing value to be filled;
when the data missing condition exists in the equipment information in the park data, cleaning is performed in a continuous value judging mode, and the expression is as follows:
wherein ,Rt For the equipment information at time t, R t-1 and Rt+1 Device state information, R, at time t-1 and time t+1, respectively N Rated information for the device.
The application further improves that: the multi-time scale data fusion module performs the following operations: selecting fixed data step length as lambda, when the time period lambda of the data to be fused Y When the time step is larger than the fixed calculation step length lambda, filling the data by adopting a Lagrange interpolation method, so that the time step length of the data is equal to lambda; when the time period lambda of the data to be fused Y When the calculated step length lambda is smaller than the fixed calculation step length lambda, the data are fused through the following algorithm:
wherein ,t+lambda for data fusion Y A data value of the time of day.
The electronic device of the application comprises a memory, a processor and a computer program stored in the memory and executable on the processor, which processor, when executing the computer program, implements the steps of the method as described above.
The computer readable storage medium of the present application stores a computer program which, when executed by a processor, implements the steps of the method as described above.
The beneficial effects of the application are as follows: according to the application, three steps of data cleaning, multi-time scale data fusion and electric carbon coupling conversion are carried out on the park data, so that the park data quality is improved, the calculation error problem caused by different time scale data is solved, and the mapping of a real physical park in a digital space is realized. Finally, a precise calculation result of the carbon emission and the carbon emission intensity of the park is obtained through an electric carbon mapping method of the digital park, low-carbon transformation and energy structure adjustment of the park can be realized, and effective support is provided for constructing electric carbon analysis and carbon peak carbon neutralization prediction of the digital park.
Drawings
FIG. 1 is a flow chart of a method for mapping digital campus electrical carbon data;
FIG. 2 is a histogram of total power usage of the campus after data cleaning;
FIG. 3 is a graph of photovoltaic power generation at a campus after multi-time scale data fusion;
figure 4 is a bar graph of carbon emissions for one day of the campus.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be noted that the embodiments described herein are only for explaining the present application and are not intended to limit the present application.
It should also be understood that the following examples are given by way of illustration only and are not to be construed as limiting the scope of the application, since various modifications and adaptations of the application may become apparent to those skilled in the art from the foregoing disclosure. The parameter settings in the specific embodiments below represent only one possible example, and those skilled in the art may make targeted modifications according to specific business scenarios.
As shown in figure 1, the digital park electric carbon data mapping method of the application realizes mapping of a real physical park in a digital space by effectively processing park data. Among these, campus data is mainly electrical quantity information and other information. The electric quantity information comprises distributed new energy information, building energy information, energy storage information and charging pile information. Other information includes campus information, equipment information, economic information, and carbon sequestration information.
And mapping the park data, wherein the mapping processing comprises three steps of data cleaning, multi-time scale data fusion and electric carbon coupling conversion.
The data cleaning is to clean the electric quantity data of the park and clean other information data of the park, wherein the cleaning mode of the electric quantity data of the park is mainly to fill up the missing value of the electric data, if a certain electric quantity data is uniformly distributed, the data is filled through a mean value, and if a certain electric quantity data is randomly distributed, the data is filled through a Lagrange interpolation method. Wherein the Lagrangian interpolation formula is as follows:
wherein ,pn (x) Is a fitting polynomial, n is the number of sampling points of a certain electric quantity data, (x) i ,y i ) Data for a single point, where x i Representing time information represented by the i-th point of the electrical quantity, y i A value indicating the i-th point of the electric quantity. L (L) n (x) And (3) interpolating a polynomial for Lagrange, wherein j is a time sequence number corresponding to a missing value to be filled. Fig. 2 is data of total electricity consumption of the campus after data cleaning, wherein 15:00 electricity consumption is filled after cleaning, and the value is 1083kWh.
The cleaning mode of other information data of the park is mainly aimed at data cleaning of equipment information, when the equipment information has data missing condition, if equipment running state information has missing in a certain time period, cleaning is carried out by a continuous value judging mode, and the formula is as follows:
wherein ,Rt For the equipment information at time t, R t-1 and Rt+1 Device state information, R, at time t-1 and time t+1, respectively N And rated information of the equipment, such as rated opening operation, rated model information and the like.
The multi-time scale data fusion step specifically comprises the following steps: firstly, selecting a fixed data step length as lambda, and when the time period lambda of the electric quantity information of certain park data Y And when the time step is larger than the fixed calculation step length lambda, filling the data by adopting a Lagrange interpolation method, so that the time step length of the data is equal to lambda. And time period lambda of electric quantity information when certain park data Y When the calculated step length lambda is smaller than the fixed calculation step length lambda, the data are fused through the following algorithm:
wherein ,t+lambda for data fusion Y A data value of the time of day. Fig. 3 is park photovoltaic power generation data after multi-time scale data fusion.
The electric carbon coupling conversion comprises electric carbon factor library introduction and scene carbon emission calculation, wherein the electric carbon factor library comprises power grid average electric carbon factor data kappa 1 And carbon sink electrical carbon factor data κ 2 . Wherein the power grid average electrical carbon factor data k 1 Taking 0.5810kg/kWh and carbon sink electric carbon factor data kappa 2 Take 4.6 t/(km) 2 Year). The scene carbon emission calculating method comprises the steps of calculating the carbon emission and the carbon emission intensity of a park scene, wherein the calculation formula is as follows:
C m (Γ)＝κ 1 [I p (Γ)-I r (Γ)]-κ 2 C e (Γ)
I p (Γ)＝I b (Γ)+I v (Γ)+I o (Γ)-I e (Γ)
wherein ,Cm (Γ) is the carbon emission of the park in period Γ, I p (Γ) is the total power consumption of the park in period Γ, I r (Γ) is the energy generation amount of the distributed new energy of the campuses in the period Γ, C e (Γ) is the total amount of carbon sequestration in the park during period Γ, I b (Γ) is the total electricity consumption of the building in the campus in period Γ, I v (Γ) is the total power consumption of the electric automobile in the campuses of the period Γ, I o (Γ) is the other electricity consumption of the park in period Γ, I e (Γ) is the energy storage and discharge capacity of the building in the park in period Γ, C o (Γ) is the carbon emission intensity of the park in period Γ, J c And (Γ) is the total yield value of the campus during period Γ. Wherein figure 4 is a plot of carbon number for one day of the campus.
It will be appreciated by those skilled in the art that embodiments of the application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. The digital park electric carbon data mapping method is characterized by comprising the following steps of: the method comprises the following steps:
step 1, acquiring park data;
step 2, cleaning the data of the park;
step 3, carrying out multi-time scale data fusion processing on the electric quantity information of the park data;
step 4, performing electric carbon coupling conversion operation, and calculating to obtain carbon emission and carbon emission intensity of the park;
the park data in the step 1 are electric quantity information and other information, wherein the electric quantity information comprises distributed new energy information, building energy information, energy storage information and charging pile information; other information includes campus information, equipment information, economic information, and carbon sequestration information.
2. The digital campus electrical carbon data mapping method of claim 1, wherein: the specific operation steps in the step 2 are as follows:
step 2.1, filling missing values of the electrical quantity information in the park data;
filling missing data by means of the average value for the uniformly distributed electrical quantity information data;
for the electrical quantity information data which are distributed randomly, filling the data by a Lagrange interpolation method, wherein the Lagrange interpolation method comprises the following expression:
wherein ,pn (x) Is a fitting polynomial, n is the number of sampling points of a certain electric quantity data, (x) i ,y i ) Data for a single point, where x i Representing time information represented by the i-th point of the electrical quantity, y i A value indicating the i-th point of the electric quantity, L n (x)The method comprises the steps of interpolating a polynomial for Lagrange, wherein j is a time sequence number corresponding to a missing value to be filled;
step 2.2, when the equipment information in the park data has the data missing condition, cleaning is performed in a continuous value judging mode, wherein the expression is as follows:
wherein ,Rt For the equipment information at time t, R t-1 and Rt+1 Device state information, R, at time t-1 and time t+1, respectively N Rated information for the device.
3. The digital campus electrical carbon data mapping method of claim 1, wherein: the step 3 specifically comprises the following steps: selecting fixed data step length as lambda, when the time period lambda of the data to be fused Y When the time step is larger than the fixed calculation step length lambda, filling the data by adopting a Lagrange interpolation method, so that the time step length of the data is equal to lambda; when the time period lambda of the data to be fused Y When the calculated step length lambda is smaller than the fixed calculation step length lambda, the data are fused through the following algorithm:
wherein ,t+lambda for data fusion Y A data value of the time of day.
4. The digital campus electrical carbon data mapping method of claim 1, wherein: the step 4 is specifically as follows:
step 4.1, importing an electric carbon factor library comprising power grid average electric carbon factor data kappa 1 And carbon sink electrical carbon factor data κ 2 ；
And 4.2, calculating the carbon emission and the carbon emission intensity of the park scene, wherein the expression is as follows:
C m (Γ)＝κ 1 [I p (Γ)-I r (Γ)]-κ 2 C e (Γ)
I p (Γ)＝I b (Γ)+I v (Γ)+I o (Γ)-I e (Γ)
wherein ,Cm (Γ) is the carbon emission of the park in period Γ, I p (Γ) is the total power consumption of the park in period Γ, I r (Γ) is the energy generation amount of the distributed new energy of the campuses in the period Γ, C e (Γ) is the total amount of carbon sequestration in the park during period Γ, I b (Γ) is the total electricity consumption of the building in the campus in period Γ, I v (Γ) is the total power consumption of the electric automobile in the campuses of the period Γ, I o (Γ) is the other electricity consumption of the park in period Γ, I e (Γ) is the energy storage and discharge capacity of the building in the park in period Γ, C o (Γ) is the carbon emission intensity of the park in period Γ, J c And (Γ) is the total yield value of the campus during period Γ.
5. Digital garden electricity carbon data mapping system, its characterized in that: the system comprises a data acquisition module, a data cleaning module, a multi-time-scale data fusion module, an electric carbon factor bank storage module and a scene carbon bank calculation module; the data acquisition module is used for acquiring park data comprising electric quantity information and other information, wherein the electric quantity information comprises distributed new energy information, building energy information, energy storage information and charging pile information; other information includes campus information, equipment information, economic information, and carbon sequestration information;
the data cleaning module is used for cleaning missing data of the electrical quantity information and the equipment information;
the multi-time scale data fusion module fuses the electrical quantity information of the park data;
the electric carbon factor library storage module is used for storing electric carbon factor data, including power grid average electric carbon factor data kappa 1 And carbon sink electrical carbon factor data κ 2 ；
The scene carbon emission calculation module is used for calculating the carbon emission and the carbon emission intensity of the park scene, and the calculation is as follows:
C m (Γ)＝κ 1 [I p (Γ)-I r (Γ)]-κ 2 C e (Γ)
I p (Γ)＝I b (Γ)+I v (Γ)+I o (Γ)-I e (Γ)
wherein ,Cm (Γ) is the carbon emission of the park in period Γ, I p (Γ) is the total power consumption of the park in period Γ, I r (Γ) is the energy generation amount of the distributed new energy of the campuses in the period Γ, C e (Γ) is the total amount of carbon sequestration in the park during period Γ, I b (Γ) is the total electricity consumption of the building in the campus in period Γ, I v (Γ) is the total power consumption of the electric automobile in the campuses of the period Γ, I o (Γ) is the other electricity consumption of the park in period Γ, I e (Γ) is the energy storage and discharge capacity of the building in the park in period Γ, C o (Γ) is the carbon emission intensity of the park in period Γ, J c And (Γ) is the total yield value of the campus during period Γ.
6. The digital campus electrical carbon data mapping system of claim 5, wherein:
the data cleaning module specifically performs the following operations:
filling missing data by means of the average value for the uniformly distributed electrical quantity information data;
for the electrical quantity information data which are distributed randomly, filling the data by a Lagrange interpolation method, wherein the Lagrange interpolation method comprises the following expression:
wherein ,pn (x) Is a fitting polynomial, n is the number of sampling points of a certain electric quantity data, (x) i ,y i ) Data for a single point, where x i Representing time information represented by the i-th point of the electrical quantity, y i A value indicating the i-th point of the electric quantity, L n (x) The method comprises the steps of interpolating a polynomial for Lagrange, wherein j is a time sequence number corresponding to a missing value to be filled;
when the data missing condition exists in the equipment information in the park data, cleaning is performed in a continuous value judging mode, and the expression is as follows:
wherein ,Rt For the equipment information at time t, R t-1 and Rt+1 Device state information, R, at time t-1 and time t+1, respectively N Rated information for the device.
7. The digital campus electrical carbon data mapping system of claim 5, wherein: the multi-time scale data fusion module performs the following operations:
selecting fixed data step length as lambda, when the time period lambda of the data to be fused Y When the time step is larger than the fixed calculation step length lambda, filling the data by adopting a Lagrange interpolation method, so that the time step length of the data is equal to lambda; when the time period lambda of the data to be fused Y When the calculated step length lambda is smaller than the fixed calculation step length lambda, the data are fused through the following algorithm:
wherein ,t+lambda for data fusion Y A data value of the time of day.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized by: the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 4.
9. A computer-readable storage medium storing a computer program, characterized in that: the computer program implementing the steps of the method according to any one of claims 1 to 4 when executed by a processor.
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